Merge pull request #60 from brainglobe/run-pre-commit

Run `pre-commit` and fix besides `mypy`

.github/workflows/test_and_deploy.yml

@@ -74,7 +74,7 @@ jobs:
 
   deploy:
     # this will run when you have tagged a commit, starting with "v*"
-    # and requires that you have put your twine API key in your 
+    # and requires that you have put your twine API key in your
     # github secrets (see readme for details)
     needs: [test]
     runs-on: ubuntu-latest

README.md

@@ -17,8 +17,8 @@
 
 ### Efficient cell detection in large images (e.g. whole mouse brain images)
 
-`cellfinder-napari` is a front-end to [cellfinder-core](https://github.com/brainglobe/cellfinder-core) to allow ease of use within the [napari](https://napari.org/index.html) multidimensional image viewer. For more details on this approach, please see [Tyson, Rousseau & Niedworok et al. (2021)](https://doi.org/10.1371/journal.pcbi.1009074). This algorithm can also be used within the original 
-[cellfinder](https://github.com/brainglobe/cellfinder) software for 
+`cellfinder-napari` is a front-end to [cellfinder-core](https://github.com/brainglobe/cellfinder-core) to allow ease of use within the [napari](https://napari.org/index.html) multidimensional image viewer. For more details on this approach, please see [Tyson, Rousseau & Niedworok et al. (2021)](https://doi.org/10.1371/journal.pcbi.1009074). This algorithm can also be used within the original
+[cellfinder](https://github.com/brainglobe/cellfinder) software for
 whole-brain microscopy analysis.
 
 `cellfinder-napari`, `cellfinder` and `cellfinder-core` were developed by [Charly Rousseau](https://github.com/crousseau) and [Adam Tyson](https://github.com/adamltyson) in the [Margrie Lab](https://www.sainsburywellcome.org/web/groups/margrie-lab), based on previous work by [Christian Niedworok](https://github.com/cniedwor), generously supported by the [Sainsbury Wellcome Centre](https://www.sainsburywellcome.org/web/).
@@ -32,46 +32,46 @@ whole-brain microscopy analysis.
 ## Instructions
 
 ### Installation
-Once you have [installed napari](https://napari.org/index.html#installation). 
-You can install napari either through the napari plugin installation tool, or 
+Once you have [installed napari](https://napari.org/index.html#installation).
+You can install napari either through the napari plugin installation tool, or
 directly from PyPI with:
 ```bash
 pip install cellfinder-napari
 ```
 
 ### Usage
-Full documentation can be 
-found [here](https://docs.brainglobe.info/cellfinder-napari). 
- 
-This software is at a very early stage, and was written with our data in mind. 
-Over time we hope to support other data types/formats. If you have any 
-questions or issues, please get in touch [on the forum](https://forum.image.sc/tag/brainglobe) or by 
+Full documentation can be
+found [here](https://docs.brainglobe.info/cellfinder-napari).
+
+This software is at a very early stage, and was written with our data in mind.
+Over time we hope to support other data types/formats. If you have any
+questions or issues, please get in touch [on the forum](https://forum.image.sc/tag/brainglobe) or by
 [raising an issue](https://github.com/brainglobe/cellfinder-napari/issues).
 
 
 ---
 ## Illustration
 
 ### Introduction
-cellfinder takes a stitched, but otherwise raw dataset with at least 
+cellfinder takes a stitched, but otherwise raw dataset with at least
 two channels:
  * Background channel (i.e. autofluorescence)
  * Signal channel, the one with the cells to be detected:
- 
+
 ![raw](https://raw.githubusercontent.com/brainglobe/cellfinder/master/resources/raw.png)
 **Raw coronal serial two-photon mouse brain image showing labelled cells**
 
 
 ### Cell candidate detection
-Classical image analysis (e.g. filters, thresholding) is used to find 
+Classical image analysis (e.g. filters, thresholding) is used to find
 cell-like objects (with false positives):
 
 ![raw](https://raw.githubusercontent.com/brainglobe/cellfinder/master/resources/detect.png)
 **Candidate cells (including many artefacts)**
 
 
 ### Cell candidate classification
-A deep-learning network (ResNet) is used to classify cell candidates as true 
+A deep-learning network (ResNet) is used to classify cell candidates as true
 cells or artefacts:
 
 ![raw](https://raw.githubusercontent.com/brainglobe/cellfinder/master/resources/classify.png)
@@ -85,7 +85,7 @@ If you find this plugin useful, and use it in your research, please cite the pre
 
 
 **If you use this, or any other tools in the brainglobe suite, please
- [let us know](mailto:[email protected]?subject=cellfinder-napari), and 
+ [let us know](mailto:[email protected]?subject=cellfinder-napari), and
  we'd be happy to promote your paper/talk etc.**
 
 ---

cellfinder_napari/curation.py

@@ -1,23 +1,17 @@
-import tifffile
+from pathlib import Path
+
 import napari
 import numpy as np
-from qtpy import QtCore
-from pathlib import Path
-from napari.qt.threading import thread_worker
-from qtpy.QtWidgets import (
-    QLabel,
-    QWidget,
-    QFileDialog,
-    QGridLayout,
-    QGroupBox,
-)
+import tifffile
 from brainglobe_napari_io.cellfinder.utils import convert_layer_to_cells
 from imlib.cells.cells import Cell
 from imlib.general.system import ensure_directory_exists
 from imlib.IO.yaml import save_yaml
+from napari.qt.threading import thread_worker
+from qtpy import QtCore
+from qtpy.QtWidgets import QFileDialog, QGridLayout, QGroupBox, QLabel, QWidget
 
-from .utils import add_combobox, add_button, display_question, display_info
-
+from .utils import add_button, add_combobox, display_info, display_question
 
 # Constants used throughout
 WINDOW_HEIGHT = 750
@@ -501,9 +495,7 @@ def extract_cubes(
     cube_height,
     cube_depth,
 ):
-    from cellfinder_core.classify.cube_generator import (
-        CubeGeneratorFromFile,
-    )
+    from cellfinder_core.classify.cube_generator import CubeGeneratorFromFile
 
     to_extract = {
         "cells": cells_to_extract,

cellfinder_napari/detect.py

@@ -1,8 +1,9 @@
-import napari
 from pathlib import Path
-from magicgui import magicgui
 from typing import List
 
+import napari
+from magicgui import magicgui
+
 from cellfinder_napari.utils import brainglobe_logo
 
 # TODO:
@@ -21,12 +22,14 @@
 def detect():
     from math import ceil
 
+    from cellfinder_core.classify.cube_generator import get_cube_depth_min_max
+    from cellfinder_core.main import main as cellfinder_run
+    from imlib.cells.cells import Cell
+
     # from fancylog import fancylog
     # import cellfinder_napari as program_for_log
     from napari.qt.threading import thread_worker
-    from cellfinder_core.main import main as cellfinder_run
-    from cellfinder_core.classify.cube_generator import get_cube_depth_min_max
-    from imlib.cells.cells import Cell
+
     from .utils import cells_to_array
 
     DEFAULT_PARAMETERS = dict(

cellfinder_napari/plugins.py

@@ -1,6 +1,6 @@
-from cellfinder_napari.train import train
-from cellfinder_napari.detect import detect
 from cellfinder_napari.curation import CurationWidget
+from cellfinder_napari.detect import detect
+from cellfinder_napari.train import train
 
 
 def napari_experimental_provide_dock_widget():

cellfinder_napari/train.py

@@ -1,6 +1,8 @@
 from pathlib import Path
+
 from magicgui import magicgui
 from napari.qt.threading import thread_worker
+
 from cellfinder_napari.utils import brainglobe_logo
 
 

cellfinder_napari/utils.py

@@ -1,15 +1,7 @@
 import pandas as pd
-from pkg_resources import resource_filename
-
-from qtpy.QtWidgets import (
-    QPushButton,
-    QLabel,
-    QComboBox,
-    QMessageBox,
-)
-
 from imlib.cells.cells import Cell
-
+from pkg_resources import resource_filename
+from qtpy.QtWidgets import QComboBox, QLabel, QMessageBox, QPushButton
 
 brainglobe_logo = resource_filename(
     "cellfinder_napari", "images/brainglobe.png"

setup.cfg

@@ -4,14 +4,14 @@ commit = True
 tag = True
 tag_name = {new_version}
 parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)(\-(?P<release>[a-z]+)(?P<rc>\d+))?
-serialize = 
+serialize =
 	{major}.{minor}.{patch}-{release}{rc}
 	{major}.{minor}.{patch}
 
 [bumpversion:part:release]
 optional_value = prod
 first_value = rc
-values = 
+values =
 	rc
 	prod
 
@@ -31,7 +31,7 @@ max-line-length = 79
 exclude = __init__.py,build,.eggs
 
 [check-manifest]
-ignore = 
+ignore =
 	resources/*
 	.pre-commit-config.yaml
 	tox.ini

setup.py

@@ -1,6 +1,7 @@
-from setuptools import setup, find_packages
 from os import path
 
+from setuptools import find_packages, setup
+
 this_directory = path.abspath(path.dirname(__file__))
 with open(path.join(this_directory, "README.md"), encoding="utf-8") as f:
     long_description = f.read()
@@ -60,9 +61,9 @@
         "Operating System :: OS Independent",
     ],
     entry_points={
-             "napari.manifest": [
-                 "cellfinder-napari = cellfinder_napari:napari.yaml",
-             ],
-         },
-         package_data={"cellfinder_napari": ["napari.yaml"]},
+        "napari.manifest": [
+            "cellfinder-napari = cellfinder_napari:napari.yaml",
+        ],
+    },
+    package_data={"cellfinder_napari": ["napari.yaml"]},
 )

tox.ini

@@ -7,25 +7,25 @@ python =
     3.7: py37
     3.8: py38
     3.9: py39
-    
+
 [gh-actions:env]
 PLATFORM =
     ubuntu-latest: linux
     macos-latest: macos
     windows-latest: windows
 
 [testenv]
-platform = 
+platform =
     macos: darwin
     linux: linux
     windows: win32
-passenv = 
+passenv =
     CI
     GITHUB_ACTIONS
     DISPLAY XAUTHORITY
     NUMPY_EXPERIMENTAL_ARRAY_FUNCTION
     PYVISTA_OFF_SCREEN
-deps = 
+deps =
     pytest  # https://docs.pytest.org/en/latest/contents.html
     pytest-cov  # https://pytest-cov.readthedocs.io/en/latest/
     pytest-xvfb ; sys_platform == 'linux'